Getting the most out of R

# Getting the most out of R
## Physalia Courses
### Luis D. Verde Arregoitia
### February 2021

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### About me

[<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"/></svg> @LuisDVerde](https://twitter.com/LuisDVerde)  
[<svg aria-hidden="true" role="img" viewBox="0 0 496 512" style="height:1em;width:0.97em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg> @LuisDVA](https://github.com/luisdva)  
[<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"/></svg> liomys.mx](https://liomys.mx)  
[<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg> luis@liomys.mx](mailto:luis@liomys.mx)
]

- Mammals, conservation, macroecology
- Evolution, ecomorphology
- Phylogenetic Comparative Methods
- Biogeography, R as a GIS
- R user since 2011  
- R 'blogger' since 2015
]

---

## You:

- are familiar with basic data types and objects in R

- can use functions and arguments

- have R and RStudio running

- can install and load packages

---

## More about you

(shared document)  
- When did you start using R?

- Why did you start using R?

- How did you learn?

---

# Course Overview
### 4 sessions, Mon-Thu, 14:00-20:00 UTC+01:00

---

## Monday - Introduction

- Syntax quirks and idiosyncrasies

- Milestones and changes in R through time

- R ‘dialects’

- The file system

- Directory structures, file paths and names

- Project and workflow organization

- Projects, 📦 {`here`}  and relative paths

---

## Tuesday - Usable data

- Organizing data in spreadsheets

- Principles of rectangular data

- Tools for data rectangling (tidyverse-oriented)

- Data types and missing values

---

## Wednesday - Increasing efficiency

- Iteration, writing loops and using 📦 {`purrr`}

- Apply functions to many things at once

- Reading many files at once

- Modifying and exporting multiple objects

- Useful addins and helpers

- Regular expressions for working with text strings

---

## Thursday - Overcoming errors and getting unstuck

- Friendly online resources

- Building web searches to solve common problems

- Identifying the best solutions

- Creating reproducible examples with the 📦 {`reprex`} package

---

# Not part of this course

- Making plots & maps

- Making nice-looking tables

- Statistical analyses

- Package development

- RMarkdown

---

## Sessions and structure

- Exercises

- Challenges

- Live coding

- Breaks

---

# My setup

- R v4.1.2

- RStudio 2022.02.0 "Prairie Trillium" Preview Release

- Default theme and pane layout

- Linux Mint 20.3

---

# Your setup
(shared document)  
- Operating System

- R version

- IDE

- Customization (fonts, editor themes, layouts)

---

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# Syntax quirks and idiosyncrasies

---

## .tc[
_What is <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>?_]

> "At the heart of the R project is a dynamic, lazy, functional, object-oriented programming language...

>This **rather unlikely linguistic cocktail** would probably never have been prepared by computer scientists, yet the language has become surprisingly popular...

> Most sessions are interactive, the user loads data and starts by plotting the data and making various simple summaries."

.tr[
Morandat et al. (2012)  
[Evaluating the Design of the R Language](https://link.springer.com/chapter/10.1007/978-3-642-31057-7_6)
]

---

<br/>
.tr[
Roger Peng  
_Teaching R to New Users - From tapply to the Tidyverse_  
useR! 2018 Keynote  
📼 [video](https://www.youtube.com/watch?v=5033jBHFiHE)  
[summary](https://simplystatistics.org/posts/2018-07-12-use-r-keynote-2018/)
]

---

## Design principles and requirements

_programming language? interactive environment? statistical package?_

<br/>

>.b.orange[S] had its roots in data analysis... its inventors were focused on figuring out how to make data analysis easier, first for themselves, and then eventually for others.

.tr[
📖 Roger Peng (2020)  
History and Overview of R  
[R Programming for Data Science](https://bookdown.org/rdpeng/rprogdatascience/history-and-overview-of-r.html)
]

---

## Design principles and requirements

<br/><br/>
Ease the transition from user to developer, allow users to:

---

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# <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>
# .white[Development history (and how this affects how we work)]

---

.b.purple[1991-1993] Developed by Ross Ihaka and Robert Gentleman

.purple.b[1995] Established as an open source project

.purple.b[1997] R Core Team formed & CRAN established

.b.purple[2000] R v1.0.0 released

.b.purple[2003] R Foundation established

.b.purple[2004] First UseR! conference

.b.purple[2015] Foundation of the R Consortium

.b.purple[2017] CRAN exceeds 10,000 packages

---

## Changes and milestones

.b.purple[2006] - 📦`reshape` [introduced](http://had.co.nz/reshape/introduction.pdf)
  - Foundations for 'long' and 'wide' data transformations  
  
.b.purple[2007] - official release for 📦`ggplot2`  
  
.b.purple[2008] - 📦`data.table` 📼 [released](http://datascience.la/matt-dowles-data-table-talk-from-user-2014/) to CRAN
  - Extensions for the R `data.frame` object. Fast, intuitive syntax.

.b.purple[2009] - 📦`plyr` introduced
  - [The split-apply-combine strategy for data analysis](https://doi.org/10.18637/jss.v040.i01) 
  
---

## Changes and milestones

.b.purple[2010] - 📦`reshape`  rebooted (`reshape2`)

.b.purple[2011] - RStudio IDE  [released](https://www.rstudio.com/about/)

.b.purple[2012] - [RLadies-Global](https://rladies.org/about-us/history/) founded

.b.purple[2012] - Interactive web applications with   [Shiny](https://www.rstudio.com/about/)

.b.purple[2014] - 📦 `dplyr` [introduced](https://www.rstudio.com/blog/introducing-dplyr/)
  - A grammar of data manipulation; upgrade to 📦 `plyr` focused on data frames

---

## Changes and milestones

.b.purple[2014] - Chained operations with pipes introduced (📦 `magrittr` also introduced late in 2014)

.b.purple[2014] - 📦 `tidyr` [introduced](https://www.rstudio.com/blog/introducing-tidyr/)
  - Replaces 📦`reshape2`
  - _Tidy data_ formalized

.b.purple[2016-2018] - Integration with big data & machine learning libraries (Spark, Tensorflow, Arrow)

.b.purple[2017] - over 10,000 packages on CRAN

---

## Changes and milestones

.b.purple[2019] - `pivot_` functions introduced to 📦 `tidyr`  
- replacements for `spread` and `gather`

.b.purple[2020] - R v4.0.0 with `stringsAsFactors = FALSE`

.b.purple[2021] - Base pipes

`*` Constant performance upgrades with each release    
`*` Growing community and user base   
`*` Language translations and freely available material

---

# Recap

> R is a programming language and free software environment for statistical computing and graphics.

- lazy
- functional programming
- object oriented
- analyst/programmer continuum

---

### Lazy Evaluation

```r
addten <- function(x,y,z){
  x+10
}
addten(3) 
addten(3,10,10)
addten(3,"nothing","missing_variable")
```
]

**Evaluation:** process of analyzing an expression and giving the user a value (e.g. typing `2+1` in the console and pressing Enter)

**Why lazy?** Allows a program to be more efficient in interactive sessions: only the needed objects will be loaded in memory and/or looked for

---

## Object-oriented programming (OOP)

- organizes software design around data, or objects, rather than functions and logic  
<br/>

]

.tr[
Homer White (2021)  
[Object-Oriented Programming in R](https://homerhanumat.github.io/r-notes/oo.html)  
Beginning Computer Science with R  
]

---

## Functional programming

In R, functional programming is much more important than object-oriented programming. We typically solve complex problems by decomposing them into simple **functions**, not simple objects. 
.tr[
Wickham (2019)  
[Advanced R- 2nd Edition](https://adv-r.hadley.nz/oo.html)
]

Functions in R are ubiquitous
> _Everything that happens (in R) is a function call_

.tr[
John Chambers  
[Extending R](https://www.routledge.com/Extending-R/Chambers/p/book/9781498775717)
]

---
class: inverse

```r
my_example
```

```
##   A B
## 1 5 D
## 2 4 G
## 3 7 H
```

## How many functions are being called here?

```r
my_numbers <- my_example[,1]
```

\* _We'll look at assignments later_

---

## and here?

```r
my_numbers_summed <- sum(my_example[,1])
```

---

## Why ~~some people dislike~~ <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> can be hard to learn

.pull-left[
- full of ambiguity and special cases...  
- slow
- packages change all the time
- The syntax is inconsistent
- .orange.b[many ways of doing the same thing]
]

.pull-right[
- unsuitable for software development
- .orange.b[too many 'dialects']
- Third party package bloat 
- Unhelpful help files
]

---

### Many ways of doing the same thing

```r
snacks <- data.frame(flavor=c("chocolate","strawberry","vanilla"),
                     calories=c(12,13,19),
                     in_stock=c(TRUE,TRUE,FALSE))
snacks
```

```
##       flavor calories in_stock
## 1  chocolate       12     TRUE
## 2 strawberry       13     TRUE
## 3    vanilla       19    FALSE
```

.tr[
Tal Yarkoni (2012)  
[R, the master troll of statistical languages](https://www.talyarkoni.org/blog/2012/06/08/r-the-master-troll-of-statistical-languages/)
]

---
class: inverse

```r
snacks
```

```
##       flavor calories in_stock
## 1  chocolate       12     TRUE
## 2 strawberry       13     TRUE
## 3    vanilla       19    FALSE
```

### Get the values of the column _flavor_ as a character vector

---

```r
snacks[,1]
snacks[1]$flavor
snacks$flavor
snacks[,'flavor']
snacks[,c('flavor')]
snacks[['flavor']]
```

```r
snacks[,1]; snacks$flavor; snacks[,'flavor']
```

```
## [1] "chocolate"  "strawberry" "vanilla"
```

---

# Can we come up with **5** more ways to extract these values?

---

## Assignment operators

_Assign a value to a name in an environment_

.tr[
Colin Fay (2018)  
[Why do we use arrow as an assignment operator?](https://colinfay.me/r-assignment/)  
]

---

### Assignment

```r
x <- 3
y = 3
3 -> z

x; y; z 
```

```
## [1] 3
```

```
## [1] 3
```
]

```r
assign('w',3)
assign("q",3)
`<-`(k,3)

w; q; k
```

```
## [1] 3
```

```
## [1] 3
```
]

---

### Right-arrow assignment

>After typing a long expression only to remember at the end that it would be a good idea to save the result, .b[`->`] allows you to perform an assignment without retyping the line.

### Useful at the end of chained operations

```r
msleep %>% 
  select(name,sleep_total) %>% 
  na.omit() -> msleep_subset
```

\*_We'll examine pipes later in this session_

---

```r
assign('w',3) == assign("w",3) 
```

```
## [1] TRUE
```

# Quotes

Single (`'`) and double (`"`) quotes can be used interchangeably\* but double quotes are preferred. .underline[Single quotes are normally only used to delimit character constants containing double quotes]

\* _Interchangeable as long as the same opening and closing quotes are used to delimit a character constant_

---

```r
c('dog","cat","mouse") # won't work
```

```r
c("dog","cat","mouse")
```

```
## [1] "dog"   "cat"   "mouse"
```

Character constants are always printed using double quotes

```r
c("dog",'cat',"mouse")
```

```
## [1] "dog"   "cat"   "mouse"
```

---

```r
print("she said yes")
```

```
## [1] "she said yes"
```

```r
print("she said "yes"") # will throw error
```

```
## Error: <text>:1:18: unexpected symbol
## 1: print("she said "yes
##                      ^
```

```r
print('she said "yes"') # works
```

```
## [1] "she said \"yes\""
```

---

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# Tools, R _dialects_, & code conventions

---

- IDEs

- Pipes

- Base? `tidyverse`? `data.table`?

- `attach`ing objects & namespacing packages

- tabs vs spaces, dots, dashes, underscores & lettercase

- customization

---

.pull-left[
### The R console
`>`  
<img src="data:image/png;base64,#imgs/desert-gd4162a088_1920.jpg" width="80%"/>  
]

.pull-left[
### RStudio
<img src="data:image/png;base64,#imgs/cockpit-g11d804731_1920.jpg" width="50%"/>  
]

---

## IDEs - Integrated development environments

RStudio  
Emacs + ESS (https://ess.r-project.org)  
vim + Nvim-R: ([Turn Vim Into an IDE for R](https://medium.com/free-code-camp/turning-vim-into-an-r-ide-cd9602e8c217))  
Visual Studio + [RTVS](https://docs.microsoft.com/en-us/visualstudio/rtvs)

---

---

## Pipes

To perform multiple operations in sequence:

- Nested code  (results evaluated from the inside out)

- Intermediate objects  (assign objects with intermediate results and pass on to the next function in a sequence)

- Pipes  (structure sequential operations left-to-right)

---

## Nested code

```r
scores <- c(7,8,8,10,6,9,5,9,8)

round(mean(scores), digits = 2)
```

```
## [1] 7.78
```

---

## Intermediate objects

```r
mn_scrs <- mean(scores)

rd_mn_scrs <- round(mn_scrs, digits = 2)

rd_mn_scrs
```

```
## [1] 7.78
```

---

## Pipes

.fl.w-third[
Left Hand Side  
(LHS)
]

.fl.w-third[
.tc[
`pipe operator`

.b.orange[`%>%`]
from  📦 .b[`magrittr`]

.b.orange[|>] in R v >= 4.1.0  
]
]

.fl.w-third[
.tr[
Right Hand Side  
(RHS)
<br/><br/><br/><br/><br/>
]]

<br/>

Take an **object** on the .avenir[Left Hand Side] of the pipe and insert it into a **function** as an argument on the .avenir[Right Hand Side]

\* By default, .avenir[LHS] is placed as the **first** argument in the call

---

## Pipes

- Minimize the need for intermediate objects and nested code

- Make code readable

- Easy to add or remove steps

> Insert with `ctrl + shift + M`

.tr[
Adolfo Álvarez (2021)  
[The (updated) history of the pipe operator in R](
http://adolfoalvarez.cl/blog/2021-09-16-plumbers-chains-and-famous-painters-the-history-of-the-pipe-operator-in-r/)
]

---

```r
library(magrittr)
scores %>% 
  mean() %>% 
  round(digits=2)
```

```
## [1] 7.78
```

```r
scores %>% 
  mean %>% 
  round(digits=2)
```

```
## [1] 7.78
```

---

`LHS |> (or %>%) RHS(argument = 12)`

*parses to*

`RHS(LHS, argument = 12)`

> `magrittr` pipes are more permissive

When piping into a function with no arguments, parentheses are optional with **`%>%`** but required with **`|>`**

_less typing (by design) and less differentiation between functions and expressions on the RHS_

---

```r
scores |> 
  mean() |> 
  round(digits=2)
```

```
## [1] 7.78
```

```r
scores |> 
  mean |> 
  round(digits=2)
```

```
## Error: The pipe operator requires a function call as RHS
```

.tr[
Isabella Velásquez (2022)  
[Understanding the native R pipe |>](https://ivelasq.rbind.io/blog/understanding-the-r-pipe/)
]

---

## Assigment pipe

Reassigns the result of the pipe chain to the starting variable.

```r
mtcars %<>%
  mutate(mpg=mpg*2)
```
]

```r
mtcars <- mtcars %>% 
  mutate(mpg=mpg*2)
```
]

---

.center[
<blockquote class="twitter-tweet tw-align-center"><p lang="en" dir="ltr">It&#39;s amazing the things one can do in base R (without installing or loading any other <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> packages): <a href="https://t.co/UfUnYMXVzd">pic.twitter.com/UfUnYMXVzd</a></p>&mdash; __mike_kearney__ (@kearneymw) <a href="https://twitter.com/kearneymw/status/1476538812406788101?ref_src=twsrc%5Etfw">December 30, 2021</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script> 
]
---

.center[
<blockquote class="twitter-tweet tw-align-center"><p lang="en" dir="ltr">Handed office key to colleague (my mistake) as she kindly agreed to water my plants while I&#39;m gone over the break. Came back today to this. And it&#39;s excellent. <a href="https://t.co/wcFzo8wch2">pic.twitter.com/wcFzo8wch2</a></p>&mdash; Christoph Dworschak (@chris_dworschak) <a href="https://twitter.com/chris_dworschak/status/1478658825813217281?ref_src=twsrc%5Etfw">January 5, 2022</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script> 
]

---

.left-column[
## Tidyverse
<img src="data:image/png;base64,#imgs/tidyverse_celestial.png" width="100%"/>
<br/><br/>
]

.right-column[
> ... an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures. Its
primary goal is to facilitate a conversation between a human and a computer about data

encompasses the repeated tasks at the heart of every data science project:
data import, tidying, manipulation, visualization, and programming
<br/><br/>
]

.tr[
Wickham et al. (2019)  
[Welcome to the tidyverse](
https://www.theoj.org/joss-papers/joss.01686/10.21105.joss.01686.pdf)  
https://www.tidyverse.org/
]

---

## Easy and efficient manipulation of data frames
<center><img src="data:image/png;base64,#imgs/dtplyr.png" width="55%"/><center/>

Different syntax, underlying philosophies, user bases, intended functionality, learning curves, etc.

---

## `data.table`
.fl.w-40[
- uses the efficient `data.table` object
- SQL-like Syntax
- **fast**
- concise
]

.fl.w-60[
<center><img src="data:image/png;base64,#imgs/dtsyntax.png" width="90%"/><center/>  
]

.tl[
Atrebas (2020)  
[A gentle introduction to data.table](https://atrebas.github.io/post/2020-06-17-datatable-introduction/)
]

---

## `dplyr`

.fl.w-40[
- uses _tibbles_
- verbs for different data operations
- works well with pipes
- communicative  
]

.fl.w-60[
<center><img src="data:image/png;base64,#imgs/dplyr.png" width="90%"/><center/>  
<br/>
]

<br/>

Roger Peng (2020)  
[Managing Data Frames with the dplyr package](https://bookdown.org/rdpeng/rprogdatascience/managing-data-frames-with-the-dplyr-package.html)

---

<center><img src="data:image/png;base64,#imgs/dplyrmeme.jpeg" width="90%"/><center/>  
Luke Smith on [Twitter](https://twitter.com/lksmth/status/1070445331165384705?s=20&t=pH9wWW0kKeBb9oD1jkf05A). Unsure about meme context.

---

# A `data.table` and `dplyr` tour

Atrebas (2020)  
[Side by side operations](https://atrebas.github.io/post/2019-03-03-datatable-dplyr/)

Martin Chan (2020)  
[Comparing Common Operations in dplyr and data.table](https://martinctc.github.io/blog/comparing-common-operations-in-dplyr-and-data.table/)

---

![](data:image/png;base64,#imgs/filterops.png)

---

![](data:image/png;base64,#imgs/sortops.png)

---

![](data:image/png;base64,#imgs/sortops.png)

---

.left-column[
<img src="data:image/png;base64,#https://dtplyr.tidyverse.org/logo.png" width="70%"/>
]

Write `dplyr` code that is automatically translated to the equivalent, but usually much faster, `data.table` code.
]

---

# Tabs vs. spaces, dots, dashes, underscores & lettercase

- Spaces have the same length on all devices, tabs do not

- Personal preference, but avoid mixing tabs and spaces for indentation

- Let IDE tools help with styling and indentation

---

# Tabs vs. spaces, dots, dashes, underscores & lettercase

- periods in object and function names not recommended (possible clashes if R and Javascript are used together)

- prefer lowerCamelCase or snake_case (dashes not allowed)

- keep a consistent style across your codebase

.tr[
💬 R: variable naming and other conventions?  
[Software Carpentry discussion on GitHub](https://github.com/swcarpentry/DEPRECATED-bc/issues/615)
]

---

## Customization

- IDE themes  -  🔗 [tmThemeEditor](https://tmtheme-editor.herokuapp.com) by Allen Bargi

.bg-black.b--hot-pink.ba.bw1.br2.shadow-4.ph2.mt1[
.gold.code[No one at the coffee shop will know you're serious unless you have a dark background] .tr[
.light-blue[_- Jared Lander_]  
📼 [R: Then and now](https://www.youtube.com/watch?v=gjpNEVcG1nU)
]]

- Fonts and ligatures  - 🔗 [Nerd Fonts](https://www.nerdfonts.com/font-downloads)

- Pane Layouts

---

# Coding conventions

- namespacing
]

---
class: center

<blockquote class="twitter-tweet tw-align-center"><p lang="en" dir="ltr">Apologies to <a href="https://twitter.com/kobriendublin?ref_src=twsrc%5Etfw">@kobriendublin</a> for my very unprofessional cry of &quot;nooooo&quot; when he suggested using attach in <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> :)</p>&mdash; Natalie Cooper (@nhcooper123) <a href="https://twitter.com/nhcooper123/status/574162671106441216?ref_src=twsrc%5Etfw">March 7, 2015</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

<blockquote class="twitter-tweet tw-align-center" data-conversation="none" data-dnt="true"><p lang="en" dir="ltr">So many hours of my life lost on MASS:select() issues</p>&mdash; Devon Cantwell-Chavez (@devon_cantwell) <a href="https://twitter.com/devon_cantwell/status/1470542297523757064?ref_src=twsrc%5Etfw">December 13, 2021</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

---

.pull-left[
<img src="data:image/png;base64,#imgs/attachmeme.jpeg" width="70%"/>  
Amelia McNamara on [Twitter](https://twitter.com/AmeliaMN/status/1389325127834214400?s=20&t=4g_1kW08sdic5B0bCmQB1w)
]

.pull-right[
Michael J. Crawley (2007)  
[The R Book](https://www.wiley.com/en-us/The+R+Book%2C+2nd+Edition-p-9780470973929)

> "_using `attach` provides simplicity and brevity, so that we can refer to variables by name and write shorter models_"

]

---

### `attach()`?

> Attach Set of R Objects to Search Path

### [The Search Path](https://rlang.r-lib.org/reference/search_envs.html#the-search-path)

> A chain of environments that determines what objects are visible from the global workspace

### environments
List-like objects that resemble a folder on our computer, binds a set of names to a set of values

---

## names and values
.pull-left[
.smallcode[

```r
my_nums <- c(24, 7, 40)
```
<br/>
]
]

**`c(24, 7, 40)`** is the value   
<br/>
]

<div style= "float:right;position: relative">
<img src="data:image/png;base64,#imgs/bindings.png" width="65%" align="right"/>  
</div> 
name-value bindings live in environments

`search()` lists the search names of environments attached to the search path

```text
search()
#>  [1] ".GlobalEnv"        "package:rlang"     "package:stats"    
#>  [4] "package:graphics"  "package:grDevices" "package:utils"    
#>  [7] "package:datasets"  "package:methods"   "Autoloads"        
#> [10] "package:base"`

```

---

- `base::library()` calls attach a new package environment to the search path

- any list, data frame, or environment can be attached to the search path with `base::attach()`

.tr[
Matt Dray (2020)
[Owning the shame of my old R code](https://www.rostrum.blog/2020/04/17/r-self-shame/)
]

---

## Your turn

- .f3[Load any installed package and see how they become attached to the search path]

- .f3[Make any assignment, and note how you have added an entry in the global environment (.b.red[`ls()`] will show what variables and functions are defined in the current environment)]

---

## Namespaces and the ' **`::`** ' operator

Namespace - management system that provides a context for looking up the value of an object associated with a name

- allow package developers to define which functions and data will be available for users that load the package

- prevent things from breaking when function names clash

- provide a way to refer to an object within a particular package

Disambiguate package elements with '`::`' (e.g. .blue.b[`dplyr::select()`])

---

.f3[If we define our own `t()` function, namespaces prevent the new definition from taking precedence, and breaking every function that tries to transpose a matrix]

---
class: inverse

## Practice

- .f2[Load packages in different order and see how the search path changes]

- .f2[Use functions from installed packages without attaching them using namespacing]

---

class: dk-section-title
background-image:url("imgs/brigalow.jpg")
background-size: cover

# .white[Files, paths, and projects]

---

# The File System

.fl.w-third[
<img src="data:image/png;base64,#imgs/filenotfound.png" width="80%"/>

.small[
Monica Chin (2021)  
[File not Found](https://www.theverge.com/22684730/students-file-folder-directory-structure-education-gen-z)
]
]

.fl.w-third[
<img src="data:image/png;base64,#imgs/filesfolders.png" width="80%"/>  
.small[
Jody Macgregor (2021)  
[PC Gamer](https://www.pcgamer.com/students-dont-know-what-files-and-folders-are-professors-say/)
]
]

.fl.w-third[
<img src="data:image/png;base64,#imgs/fsmeme.png" width="60%"/>  
]

---
.center[
<blockquote class="twitter-tweet tw-align-center" data-dnt="true"><p lang="en" dir="ltr">As a liberal college professor, I am trying to indoctrinate my students into a file naming system that does not include 78 documents called “Essay” all saved to their desktop.</p>&mdash; Dr. Liz W Faber (ft. Regina &amp; Jackie 🐈🐈) (@LizWFab) <a href="https://twitter.com/LizWFab/status/1488376977958526980?ref_src=twsrc%5Etfw">February 1, 2022</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script> 
]

---

## Directory structures, file paths and names

.fl.w-third[
<img src="data:image/png;base64,#imgs/file-system.jpg" width="70%"/>  
.f5[From the Network Encyclopedia]
]

Store files in a particular location, with intentional and meaningful names

- Use short, informative names

- Avoid using names for versioning (e.g. `var_V2_final_2020`)

- Choose names that sort well and work with searches for partial matches

---

## Names

.fl.w-40[
### Good name  
`max_temp`
</br>  
</br>
`min_price_usd`  
`monthly_temp`  
`Q1-profits_2018`
]

.fl.w-60[
### Avoid  
`Maximum_temperatureC20102014`  
`max`  
`temp`  
`'Minimum Price ($) USD'`  
`'Temperature/Month (°F)'`  
`2018q1`
</br>
]

---

## 📦 {`fs`}

Work with the file system (cross-platform)

`fs` functions always return ‘tidy’ paths

Tidy paths: 
  - Always use '**`/`**' to delimit directories  
  - Never have multiple or trailing '**`/`**'

---

# Practice
_Try to only use the keyboard!_

- Create a new folder in your project's working directory ("my_new_dir")
- Check if it was created successfully
- Create two new folders in your new folder ("data" and "data2")
- Create a text file ('mydata.csv') and save it to file inside "data"
- List all the contents (hint use `recurse = TRUE`) 
- Move the text file to "data2"
- Import the data from the text file

.b--white.ba.bw1.br2.shadow-4.ph2.mt1[
.tc[
`file_` functions to work with files  
`dir_` functions for directories
]
]

---

## Project and workflow organization

.b[project]: a folder on your computer that holds all the files relevant to that particular piece of work.

.f3[
 📁 Data (or code to get data from a remote resource)  
 📁 Analysis and/or visualization scripts  
 📁 Outputs (graphs, reports, intermediate data files, etc.)
]

.pull-left[
Jenny Bryan (2017)  
[Project-oriented workflow](https://www.tidyverse.org/blog/2017/12/workflow-vs-script/)]

.pull-right[
Maëlle Salmon (2021)  
[Draw me a project](https://masalmon.eu/2021/06/30/r-projects/)
]

---

.b.f3.blue[RStudio Projects]

- dedicated R process

- file browser pointed at Project directory
  - convenient 'go-to-project directory button'

- working directory set to Project directory
  - paths become relative to project directory

---

# Save source, not the workspace

- avoid `rm(list = ls())`. Attached objects and packages persist

- Set up 'blank slate' and restart R session often
]

.tr[
Jenny Bryan & Jim Hester (2020)  
[What they forgot to teach you about R](https://rstats.wtf/save-source.html)  
Jenny Bryan (2018)    
[Zen And The aRt Of Workflow Maintenance](https://speakerdeck.com/jennybc/zen-and-the-art-of-workflow-maintenance)
]

---

.f3.b.blue[Blank slate]

do not save the workspace to an .Rdata file

at launch, do not reload the workspace from an .Rdata file.
 
.f3.b.blue[Restarting R ]
 
Ctrl+Shift+F10  
 (Windows and Linux)  
 
Command+Shift+F10 (Mac)

---
class: inverse

## Practice

- Change a system setting  
(e.g. .bg-white[`Sys.setenv(LANGUAGE="ko")`])

Compare what happens with .bg-white[`rm(list = ls())`] vs restarting the session
]

---

## 📦 .black[{here}] and relative paths

Work relative to your project root using `here()`

- figures out the top-level of your current project
  
- helps build portable file paths

- returns absolute paths (starting with `/`, `<drive letter>:\` or `\\`)

---

```r
here("analysis","script1.R")
```
would build:  
  
  
.f5[`F:\path\that\no one\else\has\my-project\analysis\script1.R`]

---

.f3[
.b[`here()`] does not set the working directory, it simply builds a path to the **top level** of your project file every time you use it

.b[`here()`] is meant to figure out the top-level of a project (i.e. Where is the `.Rproj` file?) and build paths relative to this location

If we change the working directory with `setwd()` but nothing else changes in the project, the top-level directory remains unchanged

use .b[`dr_here()`] to get an explanation of how the top-level directory was determined
]

http://jenrichmond.rbind.io/post/how-to-use-the-here-package/